As per discussion on this SO question is NotImplementedError.
Solution/workaround is to transpose do transpose? This is used elsewhere in DataFrame.fillna method. just raise if inplace?
cc @cpcloud
In [9]: df = pd.DataFrame([[np.nan, np.nan], [np.nan, 4], [5, 6]], columns=list('AB'))
In [10]: df
Out[10]:
A B
0 NaN NaN
1 NaN 4
2 5 6
In [11]: df.mean(0)
Out[11]:
A 5
B 5
dtype: float64
In [12]: df.fillna(df.mean())
Out[12]:
A B
0 5 5
1 5 4
2 5 6
In [13]: df.mean(1)
Out[13]:
0 NaN
1 4.0
2 5.5
dtype: float64
In [14]: df.fillna(df.mean(1), axis=1)
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-14-aecc493431e2> in <module>()
----> 1 df.fillna(df.mean(1), axis=1)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/frame.pyc in fillna(self, value, method, axis, inplace, limit, downcast)
3452 if isinstance(value, (dict, Series)):
3453 if axis == 1:
-> 3454 raise NotImplementedError('Currently only can fill '
3455 'with dict/Series column '
3456 'by column')
NotImplementedError: Currently only can fill with dict/Series column by column
Comment From: jreback
this is pretty straightforward after merging in series subclass NDFrame. This has to deal with possible dtype changes when doing column wise (which I think its not-implemented)
Comment From: jorisvandenbossche
Came up again at SO
Comment From: aileronajay
@jreback @jorisvandenbossche is this implemented yet? I was trying to do this result.fillna(result.mean(axis=1), axis =1 ) and got the same exception
Comment From: jreback
issue is open
Comment From: aspiringguru
my simple/crude workaround below. curious why this issue is still open in 2018.
colnames = list(df)
for colname in colnames: df[colname].fillna(method='bfill', inplace=True)
Comment From: jorisvandenbossche
curious why this issue is still open in 2018.
Because nobody made the effort to implement it. But you are welcome to do so.
Comment From: malbahrani
Faced the same issue. It is worth mentioning here, @hayd solution posted in StackOverflow
Thanks for the workaround Andy!
Comment From: aadarshsingh191198
This isn't implemented even in the latest version of pandas (v2.0.1). Can we reopen this issue, please?
Comment From: aftersought
To confirm, in my usage ffill(axis=1, inplace=True) works as long as all dtypes of the columns are the same; I get NotImplemented error if they have mixed dtypes.
Comment From: ShivnarenSrinivasan
I see this is still an open issue Not sure if I will be able to implement this, but assigning to myself to build on top of previous PR attempts.
take
Comment From: kdebrab
Solution/workaround is to do transpose?
We actually used transpose (i.e., df.T.fillna(df.mean(1)).T), but it proved to be really horrendous with regard to performance. This performance issue happens especially when the index of df is (much) longer than the number of columns. We solved it by using apply instead:
df_mean = df.mean(1)
df.apply(lambda col: col.fillna(df_mean))
Comment From: a18rhodes
take